# Coarse-to-Fine Decoding for Neural Semantic Parsing

arXiv: https://arxiv.org/abs/1805.04793 · HTML: https://mlsanity.com/paper/1805.04793/

## Key facts

- Cited by 110 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2018.

## Built on by (most cited first, 30 of 110)

- [Natural Language to Code Translation with Execution](https://mlsanity.com/paper/2204.11454/) (2022)
- [From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought](https://mlsanity.com/paper/2306.12672/) (2023)
- [CERT: Continual Pre-Training on Sketches for Library-Oriented Code Generation](https://mlsanity.com/paper/2206.06888/) (2022)
- [Natural SQL: Making SQL Easier to Infer from Natural Language Specifications](https://mlsanity.com/paper/2109.05153/) (2021)
- [Unlocking Compositional Generalization in Pre-trained Models Using Intermediate Representations](https://mlsanity.com/paper/2104.07478/) (2021)
- [Augmenting Operations Research with Auto-Formulation of Optimization Models from Problem Descriptions](https://mlsanity.com/paper/2209.15565/) (2022)
- [Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect](https://mlsanity.com/paper/2208.10099/) (2022)
- [Improving Compositional Generalization with Latent Structure and Data Augmentation](https://mlsanity.com/paper/2112.07610/) (2021)
- [Prompting Large Language Models With the Socratic Method](https://mlsanity.com/paper/2303.08769/) (2023)
- [Large Language Model-Aware In-Context Learning for Code Generation](https://mlsanity.com/paper/2310.09748/) (2023)
- [SpreadsheetCoder: Formula Prediction from Semi-structured Context](https://mlsanity.com/paper/2106.15339/) (2021)
- [A Survey on Table Question Answering: Recent Advances](https://mlsanity.com/paper/2207.05270/) (2022)
- [LEWIS: Levenshtein Editing for Unsupervised Text Style Transfer](https://mlsanity.com/paper/2105.08206/) (2021)
- [SeqZero: Few-shot Compositional Semantic Parsing with Sequential Prompts and Zero-shot Models](https://mlsanity.com/paper/2205.07381/) (2022)
- [ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser](https://mlsanity.com/paper/2104.04689/) (2021)
- [SQLBench: A Comprehensive Evaluation for Text-to-SQL Capabilities of Large Language Models](https://mlsanity.com/paper/2403.02951/) (2024)
- [Outline, Then Details: Syntactically Guided Coarse-To-Fine Code Generation](https://mlsanity.com/paper/2305.00909/) (2023)
- [Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey](https://mlsanity.com/paper/2310.17894/) (2023)
- [PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency](https://mlsanity.com/paper/2403.09732/) (2024)
- [Improving Text-to-SQL with Schema Dependency Learning](https://mlsanity.com/paper/2103.04399/) (2021)
- [Learning to Complete Code with Sketches](https://mlsanity.com/paper/2106.10158/) (2021)
- [Knowledge Base Question Answering: A Semantic Parsing Perspective](https://mlsanity.com/paper/2209.04994/) (2022)
- [FC-KBQA: A Fine-to-Coarse Composition Framework for Knowledge Base Question Answering](https://mlsanity.com/paper/2306.14722/) (2023)
- [UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL](https://mlsanity.com/paper/2203.07781/) (2022)
- [Reliable Text-to-SQL with Adaptive Abstention](https://mlsanity.com/paper/2501.10858/) (2025)
- [Zero-Shot Cross-lingual Semantic Parsing](https://mlsanity.com/paper/2104.07554/) (2021)
- [FORTAP: Using Formulas for Numerical-Reasoning-Aware Table Pretraining](https://mlsanity.com/paper/2109.07323/) (2021)
- [Span Pointer Networks for Non-Autoregressive Task-Oriented Semantic Parsing](https://mlsanity.com/paper/2104.07275/) (2021)
- [Translate & Fill: Improving Zero-Shot Multilingual Semantic Parsing with Synthetic Data](https://mlsanity.com/paper/2109.04319/) (2021)
- [Shellcode_IA32: A Dataset for Automatic Shellcode Generation](https://mlsanity.com/paper/2104.13100/) (2021)

## Abstract (arXiv)

Semantic parsing aims at mapping natural language utterances into structured meaning representations. In this work, we propose a structure-aware neural architecture which decomposes the semantic parsing process into two stages. Given an input utterance, we first generate a rough sketch of its meaning, where low-level information (such as variable names and arguments) is glossed over. Then, we fill in missing details by taking into account the natural language input and the sketch itself. Experimental results on four datasets characteristic of different domains and meaning representations show that our approach consistently improves performance, achieving competitive results despite the use of relatively simple decoders.